On-demand companies rely on fast, accurate and robust mapping and location technologies to provide their users with a superior experience. Find out how real-time, predictive and historical traffic data can be applied to traffic-enabled routing algorithms to influence route calculations and automatically plot multiple routes with waypoints sequencing.
Discover how HERE can help you communicate updated ETAs and provide an optimized experience to your drivers and customers.

Blockchain in Financial Services is receiving a lot of attention, especially for synchronizing financial agreements between institutions. But how can blockchain be used outside of this context? Can it apply to use cases such as identity, fraud, and AML?
Watch this short webinar to hear how blockchain can be used to solve other key issues facing the industry, about research into consensus algorithms beyond proof of work, and about myths and truths that must be considered for a successful enterprise blockchain implementation.
Speaker: Nelson Petracek, CTO, TIBCO Software

MoneyLIVE’s annual survey of over 600 banking professionals found that traditional banks face a significant challenge when it comes to building AI-powered customer journeys.
75% believe that as the use of AI intensifies, banks will struggle to recruit the necessary expertise.
84% fear regulatory and liability issues surrounding AI.
Just 7% think their organization’s use of AI is highly sophisticated.
But for banks to keep pace with challengers and FinTechs, it’s crucial that they harness this continually evolving technology.
Download this chapter of MoneyLIVE's The Future of Retail Banking Report 2018/19 now and understand how TIBCO’s Connected Intelligence Platform, with the use of AI and machine learning algorithms, can help with banks’ digital transformation needs.

The idea of load balancing is well defined in the IT world: A network device accepts traffic on behalf ofa group of servers, and distributes that traffic according to load balancing algorithms and the availabilityof the services that the servers provide. From network administrators to server administrators to applicationdevelopers, this is a generally well understood concept.

The NSA’s Information Assurance Directorate left many people scratching their heads in the winter
of 2015. The directive instructed those that follow its guidelines to postpone moving from RSA
cryptography to elliptic curve cryptography (ECC) if they hadn’t already done so.
“For those partners and vendors that have not yet made the transition to Suite B elliptic curve
algorithms, we recommend not making a significant expenditure to do so at this point but instead to
prepare for the upcoming quantum-resistant algorithm transition.”
The timing of the announcement was curious. Many in the crypto community wondered if there had been
a quantum computing breakthrough significant enough to warrant the NSA’s concern. A likely candidate
for such a breakthrough came from the University of New South Wales, Australia, where researchers
announced that they’d achieved quantum effects in silicon, which would be a massive jump forward for
quantum computing.

The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloudand what it can help organisations achieve. Talking about innovation,
security and efficiency, they put the casefor an autonomous future.

The supply chain generates huge volumes of data captured in ERP, CRM, demand planning and other systems. Download this whitepaper to learn how FusionOps Machine Learning can provide companies with a more accurate, granular understanding of their business by harmonizing these disparate data sources in the cloud, and applying machine learning algorithms.

Compression algorithms reduce the number of bits needed to represent a set of data—the higher the compression ratio, the more space this particular data reduction technique saves. During our OLTP test, the Unity array achieved a compression ratio of 3.2-to-1 on the database volumes, whereas the 3PAR array averaged a 1.3-to-1 ratio. In our data mart loading test, the 3PAR achieved a ratio of 1.4-to-1 on the database volumes, whereas the Unity array got 1.3 to 1.

Predictive analytics have been used by different industries for years to solve difficult problems that range from detecting credit card fraud to determining patient risk levels for medical conditions. It combines data mining and machine-learning technologies to create statistical models based on historical data. It then uses these models to predict future events. Extracting the power from the data requires powerful algorithms behind predictive analytics.

Find out how AdScience has been able to increase their revenue potential by five times using Clustrix to optimize bidding for their online ad broker agency. AdScience runs complicated algorithms to process bids for ad space based on click history. It's critical for AdScience to have instant access to smart data.

The misuse or takeover of privileged accounts constitutes the most common source of breaches today. CA Threat Analytics for PAM provides a continuous, intelligent monitoring capability that helps enterprises detect and stop hackers and malicious insiders before they cause damage.
The software integrates a powerful set of user behavior analytics and machine learning algorithms with the trusted controls provided by CA Privileged Access Manager (CA PAM). The result is a solution that continuously analyzes the activity of individual users, accurately detects malicious and high-risk activities and automatically triggers mitigating controls to limit damage to the enterprise.

Interest in machine learning has exploded over the past decade. You see machine learning in computer science programs, industry conferences, and the Wall Street Journal almost daily. For all the talk about machine learning, many conflate what it can do with what they wish it could do. Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled. Neural networks are one type of model for machine learning; they have been around

Monetate Intelligent Recommendations is the only solution that gives merchandisers & digital marketers the power to show contextually relevant product recommendations without burdening IT resources.
Using manually curated or algorithmically-driven recommendations, marketers can easily support even the most complex product catalogs. Our solution filters recommendations based on customer attributes (e.g. shirt size), longitudinal behaviours (e.g. browsing behaviour), and situational context (e.g. product inventory at local stores). Best of all, an orchestration layer intelligently selects which algorithms and which filters to apply in any given situation, for any particular individual.

Monetate Intelligent Recommendations automates recommendations at scale without sacrificing any of the control you require. Our proprietary algorithms know what to serve each individual shopper to maximise brand value, while still allowing the control of an unlimited number of business guardrails defined by you.

Wikibon conducted in-depth interviews with organizations that had achieved Big Data success and high rates of returns. These interviews determined an important generality: that Big Data winners focused on operationalizing and automating their Big Data projects. They used Inline Analytics to drive algorithms that directly connected to and facilitated automatic change in the operational systems-of-record. These algorithms were usually developed and supported by data tables derived using Deep Data Analytics from Big Data Hadoop systems and/or data warehouses. Instead of focusing on enlightening the few with pretty historical graphs, successful players focused on changing the operational systems for everybody and managed the feedback and improvement process from the company as a whole.

Generate rich virtual data that covers the full range of possible scenarios and provide the unconstrained access to environments needed to deliver rigorously tested applications on time and within budget. Model complex live system data and apply automated rule-learning algorithms to pay off technical debt and uncover in depth understanding of composite applications, while exposing virtual data to distributed teams on demand and avoiding testing bottlenecks.

The transition to autonomous is all around. Its capability for problem-solving has never been seen before. Its potential for creating business value from algorithms and data makes it the next big frontier for business leaders. Two industry experts have discussed Oracle Autonomous Data Warehouse Cloud and what it can help organisations achieve. Talking about innovation,security and efficiency, they put the case for an autonomous future.
Watch the webinar.

MobileIron knows that cybercriminals are continuously generating more advanced ways to steal your data by any means necessary. That’s why we are committed to continually innovating and delivering new solutions that help our customers win the race against time to get ahead of the latest mobile security threats. As part of that commitment, MobileIron Threat Defense supports the five critical steps to deploying advanced, on-device mobile security. Our solution provides a single, integrated app that delivers several key advantages:
• A single app of threat protection is fully integrated with EMM.
• No user action is required to activate or update on-device security.
• Advanced mobile security blocks known and zero-day threats across iOS and Android devices with no Internet connectivity required.
• Machine-learning algorithms instantly detect and remediate on-device DNA threats.

At Amazon, we’ve been investing deeply in AI for more than 20 years. Machine learning (ML) algorithms drive many of our internal systems, and have formed the core of our customers' experience —from the path optimization in our fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, and our new retail experience, Amazon Go.
Our mission is to share our learnings and ML capabilities as fully managed services, and put them into the hands of every executive, developer, and data scientist.

In 2017, review sources proliferated, consumers became more savvy about the validity of online reviews, and the position of Chief Experience Officer started to gain traction among locationbased organizations. ORM and SEO became increasingly intertwined as Google refined its search algorithms with a strong emphasis on reviews and star ratings.

Adobe automates the process of turning insights into action by connecting Adobe Analytics to other solutions in Adobe Experience Cloud, including Adobe Target and Adobe Audience Manager. Four features make this possible:
• Anomaly detection. The technology automatically analyzes trends and determines if they are statistically significant — in milliseconds.
• Analyze play button. With analytics, you can take insights and connect them to your email, DMP, and personalization platform in seconds.
• Intelligent alert. A built-in alerting system sends an SMS text or email when it detects an anomaly. There are also predictive algorithms that help you forecast how often the alert is likely to trigger. You can set these to only notify you of the most important changes.
• Intelligent recommendations. It’s simply impossible to manually create every alert you might need, so Adobe is building machine learning directly into analytics to analyze users’ behaviors. Like a virtual data assistant, it co

The past year ushered in some big changes for Online Reputation
Management (ORM) — and the practice has become indispensable for any
location-based enterprise.
In 2017, review sources proliferated, consumers became more savvy about the validity of online
reviews, and the position of Chief Experience Officer started to gain traction among locationbased
organizations. ORM and SEO became increasingly intertwined as Google refined its search
algorithms with a strong emphasis on reviews and star ratings.
This year, expect to see these four trends move to the forefront:
1) Google will extend its dominance in online review volume and consumer exposure, eclipsing all
other specialty sites.
2) SEO will be reinvented as user-generated reviews weigh more heavily in search rankings.
3) The voice of the customer will no longer be siloed across disconnected categories.
4) Consumer feedback from reviews and social media will drive operational improvements.

Advances in deep neural networks have ignited a new wave of algorithms and tools for data scientists to tap into their data with artificial intelligence (AI). With improved algorithms, larger data sets, and frameworks such as TensorFlow, data scientists are tackling new use cases like autonomous driving vehicles and natural language processing. Read this technical white paper to learn reasons for and benefits of an end-to-end training system. It also shows performance benchmarks based on a system that combines the NVIDIA® DGX-1™, a multi-GPU server purpose-built for deep learning applications and FlashBlade, a scale-out, high performance, dynamic data hub for the entire AI data pipeline.

"ACG Michigan, a large auto insurance underwriter in the US state of Michigan, needed a user-friendly system that would enable its agents (internal and independent) to churn out precise and consistent policy quotes and underwriting decisions. They turned to FICO Blaze Advisor decision rules management system to create an enterprise decision management framework to execute decisions.
Learn more on how FICO Blaze Advisor helped ACG Michigan automate its underwriting
About FICO
FICO (NYSE: FICO), formerly known as Fair Isaac, is a leading analytics software company, helping businesses in 90+ countries make better decisions that drive higher levels of growth, profitability and customer satisfaction. The company's groundbreaking use of Big Data and mathematical algorithms to predict consumer behavior has transformed entire industries. FICO provides analytics software and tools used across multiple industries to manage risk, fight fraud, build more profitable customer relationships, optimiz